3,088 research outputs found

    A Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

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    Adapting to regularities of the environment is critical for biological organisms to anticipate events and plan. A prominent example is the circadian rhythm corresponding to the internalization by organisms of the 2424-hour period of the Earth's rotation. In this work, we study the emergence of circadian-like rhythms in deep reinforcement learning agents. In particular, we deployed agents in an environment with a reliable periodic variation while solving a foraging task. We systematically characterize the agent's behavior during learning and demonstrate the emergence of a rhythm that is endogenous and entrainable. Interestingly, the internal rhythm adapts to shifts in the phase of the environmental signal without any re-training. Furthermore, we show via bifurcation and phase response curve analyses how artificial neurons develop dynamics to support the internalization of the environmental rhythm. From a dynamical systems view, we demonstrate that the adaptation proceeds by the emergence of a stable periodic orbit in the neuron dynamics with a phase response that allows an optimal phase synchronisation between the agent's dynamics and the environmental rhythm.Comment: ICML 202

    Accurate Battery Modelling for Control Design and Economic Analysis of Lithium-ion Battery Energy Storage Systems in Smart Grid

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    Adoption of lithium-ion battery energy storage systems (Li-ion BESSs) as a flexible energy source (FES) has been rapid, particularly for active network management (ANM) schemes to facilitate better utilisation of inverter based renewable energy sources (RES) in power systems. However, Li-ion BESSs display highly nonlinear performance characteristics, which are based on parameters such as state of charge (SOC), temperature, depth of discharge (DOD), charge/discharge rate (C-rate), and battery-aging conditions. Therefore, it is important to include the dynamic nature of battery characteristics in the process of the design and development of battery system controllers for grid applications and for techno-economic studies analyzing the BESS economic profitability. This thesis focuses on improving the design and development of Li-ion BESS controllers for ANM applications by utilizing accurate battery performance models based on the second-order equivalent-circuit dynamic battery modelling technique, which considers the SOC, C-rate, temperature, and aging as its performance affecting parameters. The proposed ANM scheme has been designed to control and manage the power system parameters within the limits defined by grid codes by managing the transients introduced due to the intermittence of RESs and increasing the RES penetration at the same time. The validation of the ANM scheme and the effectiveness of controllers that manage the flexibilities in the power system, which are a part of the energy management system (EMS) of ANM, has been validated with the help of simulation studies based on an existing real-life smart grid pilot in Finland, Sundom Smart Grid (SSG). The studies were performed with offline (short-term transient-stability analysis) and real-time (long-term transient analysis) simulations. In long-term simulation studies, the effect of battery aging has also been considered as part of the Li-ion BESS controller design; thus, its impact on the overall power system operation can be analyzed. For this purpose, aging models that can determine the evolving peak power characteristics associated with aging have been established. Such aging models are included in the control loop of the Li-ion BESS controller design, which can help analyse battery aging impacts on the power system control and stability. These analyses have been validated using various use cases. Finally, the impact of battery aging on economic profitability has been studied by including battery-aging models in techno-economic studies.Aurinkosähköjärjestelmien ja tuulivoiman laajamittainen integrointi sähkövoimajärjestelmän eri jännitetasoille on lisääntynyt nopeasti. Uusiutuva energia on kuitenkin luonteeltaan vaihtelevaa, joka voi aiheuttaa nopeita muutoksia taajuudessa ja jännitteessä. Näiden vaihteluiden hallintaan tarvitaan erilaisia joustavia energiaresursseja, kuten energiavarastoja, sekä niiden tehokkaan hyödyntämisen mahdollistaviea älykkäitä ja aktiivisia hallinta- ja ohjausjärjestelmiä. Litiumioniakkuihin pohjautuvien invertteriliitäntäisten energian varastointijärjestelmien käyttö joustoresursseina aktiiviseen verkonhallintaan niiden pätö- ja loistehon ohjauksen avulla on lisääntynyt nopeasti johtuen niiden kustannusten laskusta, modulaarisuudesta ja teknisistä ominaisuuksista. Litiumioniakuilla on erittäin epälineaariset ominaisuudet joita kuvaavat parametrit ovat esimerkiksi lataustila, lämpötila, purkaussyvyys, lataus/ purkausnopeus ja akun ikääntyminen. Akkujen ominaisuuksien dynaaminen luonne onkin tärkeää huomioida myös akkujen sähköverkkoratkaisuihin liittyvien säätöjärjestelmien kehittämisessä sekä teknis-taloudellisissa kannattavuusanalyyseissa. Tämä väitöstutkimus keskittyy ensisijaisesti aktiiviseen verkonhallintaan käytettävien litiumioniakkujen säätöratkaisuiden parantamiseen hyödyntämällä tarkkoja, dynaamisia akun suorituskykymalleja, jotka perustuvat toisen asteen ekvivalenttipiirien akkumallinnustekniikkaan, jossa otetaan huomioon lataustila, lataus/purkausnopeus ja lämpötila. Työssä kehitetyn aktiivisen verkonhallintajärjestelmän avulla tehtävät akun pätö- ja loistehon ohjausperiaatteet on validoitu laajamittaisten simulointien avulla, esimerkiksi paikallista älyverkkopilottia Sundom Smart Gridiä simuloimalla. Simuloinnit tehtiin sekä lyhyen aikavälin offline-simulaatio-ohjelmistoilla että pitkän aikavälin simulaatioilla hyödyntäen reaaliaikasimulointilaitteistoa. Pitkän aikavälin simulaatioissa akun ikääntymisen vaikutus otettiin huomioon litiumioniakun ohjauksen suunnittelussa jotta sen vaikutusta sähköjärjestelmän kokonaistoimintaan voitiin analysoida. Tätä tarkoitusta varten luotiin akun ikääntymismalleja, joilla on mahdollista määrittää akun huipputehon muutos sen ikääntyessä. Akun huipputehon muutos taas vaikuttaa sen hyödynnettävyyteen erilaisten pätötehon ohjaukseen perustuvien joustopalveluiden tarjoamiseen liittyen. Lisäksi väitöstutkimuksessa tarkasteltiin akkujen ikääntymisen vaikutusta niiden taloudelliseen kannattavuuteen sisällyttämällä akkujen ikääntymismalleja teknis-taloudellisiin tarkasteluihin.fi=vertaisarvioitu|en=peerReviewed

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Analog Photonics Computing for Information Processing, Inference and Optimisation

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    This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202
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